

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class        A__(a_							):
						"""simple docstring"""






						_A    :				Tuple       					=			(UnCLIPScheduler,)





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Optional[int]:
									a_						:						Union[str, Any] 							= {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**_lowercase    )
									return config





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									for timesteps in [1, 5, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									for variance in ["fixed_small_log", "learned_range"]:
												self.check_over_configs(variance_type=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									for clip_sample_range in [1, 5, 10, 20]:
												self.check_over_configs(clip_sample_range=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(prediction_type=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									for time_step in [0, 500, 999]:
												for prev_timestep in [None, 5, 100, 250, 500, 750]:
															if prev_timestep is not None and prev_timestep >= time_step:
																		continue

															self.check_over_forward(time_step=_lowercase  ,      prev_timestep=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[int] 							= self.scheduler_classes[0]
									a_						:						Dict 							= self.get_scheduler_config(variance_type="""fixed_small_log"""    )
									a_						:						Tuple 							= scheduler_class(**_lowercase    )

									assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0000e-10    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_5_4_9_6_2_5    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.9_9_9_4_9_8_7    )    ) < 1e-5





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Union[str, Any] 							= self.scheduler_classes[0]
									a_						:						List[str] 							= self.get_scheduler_config(variance_type="""learned_range"""    )
									a_						:						Dict 							= scheduler_class(**_lowercase    )

									a_						:						int 							= 0.5

									assert scheduler._get_variance(1  ,      predicted_variance=_lowercase    ) - -1_0.1_7_1_2_7_9_0 < 1e-5
									assert scheduler._get_variance(487  ,      predicted_variance=_lowercase    ) - -5.7_9_9_8_0_5_2 < 1e-5
									assert scheduler._get_variance(999  ,      predicted_variance=_lowercase    ) - -0.0_0_1_0_0_1_1 < 1e-5





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.scheduler_classes[0]
									a_						:						int 							= self.get_scheduler_config()
									a_						:						Any 							= scheduler_class(**_lowercase    )

									a_						:						int 							= scheduler.timesteps

									a_						:						Optional[int] 							= self.dummy_model()
									a_						:						int 							= self.dummy_sample_deter
									a_						:						List[str] 							= torch.manual_seed(0    )

									for i, t in enumerate(_lowercase    ):
												# 1. predict noise residual
												a_						:						Tuple 							= model(_lowercase  ,      _lowercase    )

												# 2. predict previous mean of sample x_t-1
												a_						:						Union[str, Any] 							= scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase    ).prev_sample

												a_						:						Optional[int] 							= pred_prev_sample

									a_						:						Tuple 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						Tuple 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5    ) < 1e-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						str 							= self.scheduler_classes[0]
									a_						:						Optional[int] 							= self.get_scheduler_config()
									a_						:						Optional[Any] 							= scheduler_class(**_lowercase    )

									scheduler.set_timesteps(25    )

									a_						:						int 							= scheduler.timesteps

									a_						:						str 							= self.dummy_model()
									a_						:						List[Any] 							= self.dummy_sample_deter
									a_						:						Optional[Any] 							= torch.manual_seed(0    )

									for i, t in enumerate(_lowercase    ):
												# 1. predict noise residual
												a_						:						str 							= model(_lowercase  ,      _lowercase    )

												if i + 1 == timesteps.shape[0]:
															a_						:						str 							= None
												else:
															a_						:						str 							= timesteps[i + 1]

												# 2. predict previous mean of sample x_t-1
												a_						:						Union[str, Any] 							= scheduler.step(
												    _lowercase  ,      _lowercase  ,      _lowercase  ,      prev_timestep=_lowercase  ,      generator=_lowercase    ).prev_sample

												a_						:						List[Any] 							= pred_prev_sample

									a_						:						Any 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						Any 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3    ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									pass





def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


